Simplifying Data Fabric through Data Discovery and Data Access

Sep 15, 2022 |
Rameez Ghous

Sr. Manager, Technical Marketing

Data Is at the Heart of Modern Business Decision-Making

We are living in a world where data surrounds us. From smartphones to wearables to sensors, devices are generating data at an ever-increasing pace. When pieced together, data can help to tell a larger story with depth and insight. In fact, machines today are data driven, and AI and ML models that are transforming the technological landscape are data driven. Businesses that can harness the raw power of data typically lead their respective industries. For example, if you check out the Fortune 500,1  you will see a clear pattern: companies that top the list are able to use data to their advantage. They are poised to become even more successful because they realize that to be the best, they must be data driven.

However, managing and using data is easier said than done. Organizations today know that data is needed for them to make the right decisions, but the ongoing (and potentially overwhelming) challenge is how to choose which data to use and where to find it. With so many applications and with data located in so many different places across the enterprise, organizations need a way to quickly and easily get the right data into the hands of business users to facilitate informed decision-making. But many of today’s business users must spend considerable time just finding the right data and then structuring it in a way so that they can use it. This creates a huge reliance on IT to provision data for business users.

Data-driven Capabilities Powered by Data Fabric

To be truly agile, modern organizations need to use their data and make the right moves responsively. For that to happen, they must provide business users with the power to access and use data. This means that business users should be able to know what data is available and be able to quickly access it without having to wait for intervention from IT.

Sounds like a utopia, right? Well, attempts have been made to turn this into reality by building centralized repositories. But this approach has had a genuine problem — most organizations have a mix of multi-cloud or hybrid cloud data infrastructure, making a centralized approach unrealistic.

Data fabric, on the other hand, does not require organizations to centralize data storage. Rather, it creates an abstraction layer for the data community by unifying diverse technology under one umbrella. The goal of data fabric is to help enable self-service data access, using no-code/low-code principles, while being heavily driven by a combination of active and passive metadata and knowledge graph.

Metadata is the real nervous system that helps run this architectural concept. Metadata helps connect various components together and helps data fabric truly flourish in a landscape populated with diverse technological solutions.

Figure 1: Pictorial representation of the data fabric building blocks Figure 1: Pictorial representation of the data fabric building blocks


Data Discovery and Data Access Are the Cornerstones of Data Fabric

Active data governance (AI-driven automated governance that learns from your data landscape to intelligently automate manual tasks and provide recommendations and key insights) , intelligent data integration, smart data quality and insightful metadata management are key building blocks that help fuel a working and thriving data fabric. But easy data discovery and data access are the capabilities that help users realize the true value of this emerging but significant architectural construct. It is this real possibility — of putting the power of data in the hands of the users — that brings about a seismic shift in how end users have traditionally interacted with data. Data fabric unlocks a whole new frontier for the data community, as it frees people from having to spend hours or even days trying to figure what data they need to meet their requirements.

With a rich data catalog, data users know what data assets and data elements are available. A data marketplace ensures that data users get context about the data they are looking for and have a way to easily shop and get access to the data that they need. A data marketplace empowers non-technical data consumers with a one-stop shop to find, understand, trust and access data, giving them a self-service capability that revolutionizes the way they interact with data. All of this is supported by robust underlying active governance and metadata capabilities, making what once seemed impossible a tangible reality.

Unlocking the True Potential of a Data-driven Organization with Data Discovery and Data Access

In essence, a data community within an organization will start appreciating the value of implementing data fabric when they have the power of data discovery and data access in the palm of their hand. It is no wonder then that data discovery and data access capabilities in data fabric provide the tangible benefits that herald the true potential of a data-driven organization by:

  • Enabling data self-service capabilities
  • Reducing the dependence of business on IT
  • Reducing the time required for analytical projects
  • Providing a one-stop shop for all things data
  • Fueling data-driven innovation
Figure 2: A view of how to discover data within a data catalog Figure 2: A view of how to discover data within a data catalog
Figure 3: A view of how to raise access to data collection within a data marketplace Figure 3: A view of how to raise access to data collection within a data marketplace

Infomatica has a compelling concept called predictive data intelligence (PDI) that can help bring this idea to fruition using the Informatica Intelligent Data Management Cloud™ (IDMC). PDI serves as the data foundation that helps a ccelerate and automate data governance, enable data sharing programs and drive analytics and business outcomes. These capabilities in turn help data-driven organizations realize value intelligently and quickly.

Figure 4: A reference architecture showcasing predictive data intelligence in action Figure 4: A reference architecture showcasing predictive data intelligence in action


With predictive data intelligence in action, users will be able to easily find, use and share data.

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